Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = '/data'
!pip install matplotlib==2.0.2
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Collecting matplotlib==2.0.2
  Downloading https://files.pythonhosted.org/packages/60/d4/6b6d8a7a6bc69a1602ab372f6fc6e88ef88a8a96398a1a25edbac636295b/matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl (14.6MB)
    100% |████████████████████████████████| 14.6MB 48kB/s  eta 0:00:01
Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
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Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.0.2)
Installing collected packages: matplotlib
  Found existing installation: matplotlib 2.1.0
    Uninstalling matplotlib-2.1.0:
      Successfully uninstalled matplotlib-2.1.0
Successfully installed matplotlib-2.0.2
You are using pip version 9.0.1, however version 10.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f3c0e79a4e0>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f3c0e704208>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels),
                                'real_inputs')
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), 'z_inputs')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 478, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 281, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 232, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 397, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2728, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2856, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-6-b95497074de8>", line 25, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse=reuse):
        
        alpha = 0.2
        
        h1 = tf.layers.conv2d(images, 64, 5, 2, 'same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, 2, 'same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, 2, 'same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        flat = tf.reshape(h3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.2
    
        h1 = tf.layers.dense(z, 2*2*512)
        h1 = tf.reshape(h1, (-1, 2, 2, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 5, 2, 'valid',kernel_initializer=tf.contrib.layers.xavier_initializer())
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 5, 2, 'same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
    
        logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, 2, 'same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        out = tf.tanh(logits)
    
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    d_train_opt = tf.train.AdamOptimizer(
        learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_updates = [opt for opt in ops if opt.name.startswith('generator')]
    with tf.control_dependencies(g_updates):
        g_train_opt = tf.train.AdamOptimizer(
            learning_rate, beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _, img_width, img_height, img_channels = data_shape
    
    real_input, z_input, lr = model_inputs(
        img_width, img_height, img_channels, z_dim)
    
    d_loss, g_loss = model_loss(real_input, z_input, img_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    print_every = 10
    show_every = 100
    losses = []
    n_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2.0
                z_sample = np.random.uniform(-1, 1, (batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={
                        real_input: batch_images, z_input: z_sample, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={
                        z_input: z_sample, lr: learning_rate})
                
                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({z_input: z_sample, real_input: batch_images})
                    train_loss_g = g_loss.eval({z_input: z_sample})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    losses.append((train_loss_d, train_loss_g))
                
                if steps % show_every == 0:
                    show_generator_output(sess, n_images, z_input, img_channels, data_image_mode)

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 16
z_dim = 128
learning_rate = 0.0002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 1.3393... Generator Loss: 2.7444
Epoch 1/2... Discriminator Loss: 1.8021... Generator Loss: 5.7551
Epoch 1/2... Discriminator Loss: 1.8385... Generator Loss: 3.0000
Epoch 1/2... Discriminator Loss: 0.6541... Generator Loss: 1.8856
Epoch 1/2... Discriminator Loss: 1.3278... Generator Loss: 4.6558
Epoch 1/2... Discriminator Loss: 1.5202... Generator Loss: 0.3942
Epoch 1/2... Discriminator Loss: 0.6512... Generator Loss: 6.8048
Epoch 1/2... Discriminator Loss: 0.5686... Generator Loss: 1.7014
Epoch 1/2... Discriminator Loss: 0.7265... Generator Loss: 1.2638
Epoch 1/2... Discriminator Loss: 1.9563... Generator Loss: 10.5000

Epoch 1/2... Discriminator Loss: 0.5029... Generator Loss: 2.3127
Epoch 1/2... Discriminator Loss: 0.5758... Generator Loss: 3.3677
Epoch 1/2... Discriminator Loss: 0.5060... Generator Loss: 2.0296
Epoch 1/2... Discriminator Loss: 0.5867... Generator Loss: 1.5718
Epoch 1/2... Discriminator Loss: 0.5671... Generator Loss: 2.0529
Epoch 1/2... Discriminator Loss: 2.6882... Generator Loss: 0.1383
Epoch 1/2... Discriminator Loss: 0.8651... Generator Loss: 1.9436
Epoch 1/2... Discriminator Loss: 0.8377... Generator Loss: 1.6479
Epoch 1/2... Discriminator Loss: 1.2042... Generator Loss: 0.7346
Epoch 1/2... Discriminator Loss: 0.8127... Generator Loss: 1.4740

Epoch 1/2... Discriminator Loss: 0.9262... Generator Loss: 1.2253
Epoch 1/2... Discriminator Loss: 0.6573... Generator Loss: 1.7401
Epoch 1/2... Discriminator Loss: 0.7345... Generator Loss: 1.8702
Epoch 1/2... Discriminator Loss: 1.1444... Generator Loss: 0.8196
Epoch 1/2... Discriminator Loss: 0.6329... Generator Loss: 2.0237
Epoch 1/2... Discriminator Loss: 2.0915... Generator Loss: 0.2344
Epoch 1/2... Discriminator Loss: 0.8642... Generator Loss: 2.1907
Epoch 1/2... Discriminator Loss: 1.0345... Generator Loss: 3.0951
Epoch 1/2... Discriminator Loss: 1.1081... Generator Loss: 0.8270
Epoch 1/2... Discriminator Loss: 1.1852... Generator Loss: 2.0276

Epoch 1/2... Discriminator Loss: 1.5523... Generator Loss: 0.4310
Epoch 1/2... Discriminator Loss: 1.0232... Generator Loss: 1.5836
Epoch 1/2... Discriminator Loss: 1.0127... Generator Loss: 2.0601
Epoch 1/2... Discriminator Loss: 1.2510... Generator Loss: 0.7913
Epoch 1/2... Discriminator Loss: 1.0140... Generator Loss: 1.3826
Epoch 1/2... Discriminator Loss: 1.0936... Generator Loss: 1.6634
Epoch 1/2... Discriminator Loss: 1.0705... Generator Loss: 1.4275
Epoch 1/2... Discriminator Loss: 0.9506... Generator Loss: 1.2191
Epoch 1/2... Discriminator Loss: 1.6118... Generator Loss: 0.5374
Epoch 1/2... Discriminator Loss: 0.9219... Generator Loss: 1.5185

Epoch 1/2... Discriminator Loss: 1.0099... Generator Loss: 1.2640
Epoch 1/2... Discriminator Loss: 0.9079... Generator Loss: 1.0446
Epoch 1/2... Discriminator Loss: 1.1885... Generator Loss: 0.8110
Epoch 1/2... Discriminator Loss: 1.0029... Generator Loss: 0.9224
Epoch 1/2... Discriminator Loss: 1.3821... Generator Loss: 0.5925
Epoch 1/2... Discriminator Loss: 1.5917... Generator Loss: 0.4076
Epoch 1/2... Discriminator Loss: 1.0291... Generator Loss: 1.8728
Epoch 1/2... Discriminator Loss: 0.8833... Generator Loss: 1.7365
Epoch 1/2... Discriminator Loss: 1.6512... Generator Loss: 0.4900
Epoch 1/2... Discriminator Loss: 0.8842... Generator Loss: 1.7971

Epoch 1/2... Discriminator Loss: 1.0337... Generator Loss: 1.0620
Epoch 1/2... Discriminator Loss: 1.3200... Generator Loss: 0.7305
Epoch 1/2... Discriminator Loss: 0.9198... Generator Loss: 1.3584
Epoch 1/2... Discriminator Loss: 1.0428... Generator Loss: 0.8774
Epoch 1/2... Discriminator Loss: 1.1311... Generator Loss: 0.8780
Epoch 1/2... Discriminator Loss: 0.9641... Generator Loss: 0.9926
Epoch 1/2... Discriminator Loss: 0.9537... Generator Loss: 0.9820
Epoch 1/2... Discriminator Loss: 1.2793... Generator Loss: 0.5698
Epoch 1/2... Discriminator Loss: 1.1211... Generator Loss: 0.7144
Epoch 1/2... Discriminator Loss: 1.5230... Generator Loss: 0.4157

Epoch 1/2... Discriminator Loss: 1.5729... Generator Loss: 0.3811
Epoch 1/2... Discriminator Loss: 1.1142... Generator Loss: 0.7570
Epoch 1/2... Discriminator Loss: 1.3926... Generator Loss: 0.5242
Epoch 1/2... Discriminator Loss: 1.1152... Generator Loss: 2.3009
Epoch 1/2... Discriminator Loss: 0.9449... Generator Loss: 1.5631
Epoch 1/2... Discriminator Loss: 0.9810... Generator Loss: 1.2444
Epoch 1/2... Discriminator Loss: 1.1888... Generator Loss: 1.4516
Epoch 1/2... Discriminator Loss: 1.3332... Generator Loss: 0.6655
Epoch 1/2... Discriminator Loss: 1.1308... Generator Loss: 0.7997
Epoch 1/2... Discriminator Loss: 1.1994... Generator Loss: 0.6367

Epoch 1/2... Discriminator Loss: 1.0655... Generator Loss: 0.8648
Epoch 1/2... Discriminator Loss: 1.1357... Generator Loss: 0.8359
Epoch 1/2... Discriminator Loss: 0.9901... Generator Loss: 1.0583
Epoch 1/2... Discriminator Loss: 1.1635... Generator Loss: 1.3072
Epoch 1/2... Discriminator Loss: 0.9672... Generator Loss: 1.5598
Epoch 1/2... Discriminator Loss: 1.2494... Generator Loss: 0.6483
Epoch 1/2... Discriminator Loss: 0.8725... Generator Loss: 1.2323
Epoch 1/2... Discriminator Loss: 1.3149... Generator Loss: 2.5222
Epoch 1/2... Discriminator Loss: 1.5076... Generator Loss: 0.4078
Epoch 1/2... Discriminator Loss: 1.1028... Generator Loss: 2.5706

Epoch 1/2... Discriminator Loss: 1.1450... Generator Loss: 0.9648
Epoch 1/2... Discriminator Loss: 0.9460... Generator Loss: 1.1715
Epoch 1/2... Discriminator Loss: 0.9151... Generator Loss: 1.4707
Epoch 1/2... Discriminator Loss: 1.3725... Generator Loss: 0.5159
Epoch 1/2... Discriminator Loss: 1.0805... Generator Loss: 1.5525
Epoch 1/2... Discriminator Loss: 1.0867... Generator Loss: 1.3221
Epoch 1/2... Discriminator Loss: 1.2619... Generator Loss: 0.5898
Epoch 1/2... Discriminator Loss: 1.2923... Generator Loss: 0.5771
Epoch 1/2... Discriminator Loss: 1.5018... Generator Loss: 0.4422
Epoch 1/2... Discriminator Loss: 1.2088... Generator Loss: 1.3034

Epoch 1/2... Discriminator Loss: 0.9111... Generator Loss: 1.1782
Epoch 1/2... Discriminator Loss: 1.0919... Generator Loss: 0.7880
Epoch 1/2... Discriminator Loss: 0.9683... Generator Loss: 1.2434
Epoch 1/2... Discriminator Loss: 1.2375... Generator Loss: 0.6566
Epoch 1/2... Discriminator Loss: 1.0557... Generator Loss: 0.8324
Epoch 1/2... Discriminator Loss: 1.4139... Generator Loss: 0.5169
Epoch 1/2... Discriminator Loss: 1.2107... Generator Loss: 0.6251
Epoch 1/2... Discriminator Loss: 1.2510... Generator Loss: 2.0921
Epoch 1/2... Discriminator Loss: 0.8214... Generator Loss: 1.6458
Epoch 1/2... Discriminator Loss: 1.5080... Generator Loss: 0.4981

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 16
z_dim = 128
learning_rate = 0.0002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/1... Discriminator Loss: 3.6038... Generator Loss: 0.0498
Epoch 1/1... Discriminator Loss: 0.8452... Generator Loss: 1.2513
Epoch 1/1... Discriminator Loss: 2.7514... Generator Loss: 0.1137
Epoch 1/1... Discriminator Loss: 0.7160... Generator Loss: 2.6804
Epoch 1/1... Discriminator Loss: 1.6826... Generator Loss: 9.2876
Epoch 1/1... Discriminator Loss: 0.9872... Generator Loss: 0.9197
Epoch 1/1... Discriminator Loss: 1.2202... Generator Loss: 5.3010
Epoch 1/1... Discriminator Loss: 1.0653... Generator Loss: 1.3826
Epoch 1/1... Discriminator Loss: 0.5354... Generator Loss: 2.7873
Epoch 1/1... Discriminator Loss: 0.8514... Generator Loss: 1.2355

Epoch 1/1... Discriminator Loss: 0.5669... Generator Loss: 2.9244
Epoch 1/1... Discriminator Loss: 0.6420... Generator Loss: 1.7424
Epoch 1/1... Discriminator Loss: 1.0241... Generator Loss: 0.7623
Epoch 1/1... Discriminator Loss: 0.5458... Generator Loss: 2.2867
Epoch 1/1... Discriminator Loss: 0.3742... Generator Loss: 3.7763
Epoch 1/1... Discriminator Loss: 0.5432... Generator Loss: 2.3575
Epoch 1/1... Discriminator Loss: 0.4464... Generator Loss: 3.0226
Epoch 1/1... Discriminator Loss: 0.6418... Generator Loss: 1.5397
Epoch 1/1... Discriminator Loss: 0.4735... Generator Loss: 2.4963
Epoch 1/1... Discriminator Loss: 0.7109... Generator Loss: 1.8827

Epoch 1/1... Discriminator Loss: 0.6482... Generator Loss: 1.5558
Epoch 1/1... Discriminator Loss: 0.4581... Generator Loss: 4.0296
Epoch 1/1... Discriminator Loss: 1.3137... Generator Loss: 0.5162
Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 3.8143
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 4.2364
Epoch 1/1... Discriminator Loss: 1.1181... Generator Loss: 0.6445
Epoch 1/1... Discriminator Loss: 1.4679... Generator Loss: 0.5379
Epoch 1/1... Discriminator Loss: 1.1465... Generator Loss: 0.6681
Epoch 1/1... Discriminator Loss: 0.8817... Generator Loss: 2.2752
Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.7864

Epoch 1/1... Discriminator Loss: 0.8551... Generator Loss: 1.7315
Epoch 1/1... Discriminator Loss: 1.3709... Generator Loss: 0.6100
Epoch 1/1... Discriminator Loss: 1.2219... Generator Loss: 0.6636
Epoch 1/1... Discriminator Loss: 0.9791... Generator Loss: 0.9197
Epoch 1/1... Discriminator Loss: 1.0509... Generator Loss: 1.7148
Epoch 1/1... Discriminator Loss: 1.3347... Generator Loss: 0.6170
Epoch 1/1... Discriminator Loss: 1.2641... Generator Loss: 0.9701
Epoch 1/1... Discriminator Loss: 1.4942... Generator Loss: 0.4693
Epoch 1/1... Discriminator Loss: 1.3623... Generator Loss: 0.9408
Epoch 1/1... Discriminator Loss: 1.2639... Generator Loss: 0.9983

Epoch 1/1... Discriminator Loss: 1.0038... Generator Loss: 1.0862
Epoch 1/1... Discriminator Loss: 1.0702... Generator Loss: 0.9290
Epoch 1/1... Discriminator Loss: 1.1516... Generator Loss: 0.9043
Epoch 1/1... Discriminator Loss: 1.3183... Generator Loss: 0.7535
Epoch 1/1... Discriminator Loss: 1.0788... Generator Loss: 1.8678
Epoch 1/1... Discriminator Loss: 0.9758... Generator Loss: 1.0075
Epoch 1/1... Discriminator Loss: 1.0707... Generator Loss: 0.9439
Epoch 1/1... Discriminator Loss: 1.3080... Generator Loss: 0.9983
Epoch 1/1... Discriminator Loss: 1.1250... Generator Loss: 1.0534
Epoch 1/1... Discriminator Loss: 1.5123... Generator Loss: 0.4506

Epoch 1/1... Discriminator Loss: 1.3271... Generator Loss: 1.4776
Epoch 1/1... Discriminator Loss: 1.1311... Generator Loss: 0.7824
Epoch 1/1... Discriminator Loss: 1.1951... Generator Loss: 0.7235
Epoch 1/1... Discriminator Loss: 1.2256... Generator Loss: 0.7164
Epoch 1/1... Discriminator Loss: 1.3558... Generator Loss: 1.3649
Epoch 1/1... Discriminator Loss: 1.1714... Generator Loss: 1.2283
Epoch 1/1... Discriminator Loss: 1.3566... Generator Loss: 0.6745
Epoch 1/1... Discriminator Loss: 1.2796... Generator Loss: 0.6758
Epoch 1/1... Discriminator Loss: 1.3574... Generator Loss: 0.5881
Epoch 1/1... Discriminator Loss: 0.9595... Generator Loss: 0.9768

Epoch 1/1... Discriminator Loss: 1.0313... Generator Loss: 0.8566
Epoch 1/1... Discriminator Loss: 1.2563... Generator Loss: 0.7198
Epoch 1/1... Discriminator Loss: 1.1734... Generator Loss: 0.8013
Epoch 1/1... Discriminator Loss: 0.9400... Generator Loss: 1.2811
Epoch 1/1... Discriminator Loss: 1.0014... Generator Loss: 1.0191
Epoch 1/1... Discriminator Loss: 1.3857... Generator Loss: 0.5126
Epoch 1/1... Discriminator Loss: 1.3651... Generator Loss: 0.6148
Epoch 1/1... Discriminator Loss: 1.2774... Generator Loss: 1.3715
Epoch 1/1... Discriminator Loss: 1.2274... Generator Loss: 1.1100
Epoch 1/1... Discriminator Loss: 1.4770... Generator Loss: 0.6429

Epoch 1/1... Discriminator Loss: 1.4389... Generator Loss: 0.6221
Epoch 1/1... Discriminator Loss: 1.3302... Generator Loss: 0.6356
Epoch 1/1... Discriminator Loss: 1.2061... Generator Loss: 0.6442
Epoch 1/1... Discriminator Loss: 1.4593... Generator Loss: 0.5829
Epoch 1/1... Discriminator Loss: 1.2800... Generator Loss: 0.8874
Epoch 1/1... Discriminator Loss: 1.0513... Generator Loss: 0.8548
Epoch 1/1... Discriminator Loss: 1.0222... Generator Loss: 0.9775
Epoch 1/1... Discriminator Loss: 1.2556... Generator Loss: 0.6959
Epoch 1/1... Discriminator Loss: 1.1855... Generator Loss: 0.7504
Epoch 1/1... Discriminator Loss: 1.3964... Generator Loss: 1.0676

Epoch 1/1... Discriminator Loss: 1.2037... Generator Loss: 0.6946
Epoch 1/1... Discriminator Loss: 1.2416... Generator Loss: 0.7983
Epoch 1/1... Discriminator Loss: 1.1753... Generator Loss: 1.0667
Epoch 1/1... Discriminator Loss: 1.3831... Generator Loss: 0.8395
Epoch 1/1... Discriminator Loss: 1.3996... Generator Loss: 0.5655
Epoch 1/1... Discriminator Loss: 1.2013... Generator Loss: 0.7349
Epoch 1/1... Discriminator Loss: 1.1853... Generator Loss: 0.7288
Epoch 1/1... Discriminator Loss: 1.1084... Generator Loss: 1.0485
Epoch 1/1... Discriminator Loss: 1.2073... Generator Loss: 2.1777
Epoch 1/1... Discriminator Loss: 1.1121... Generator Loss: 1.1138

Epoch 1/1... Discriminator Loss: 1.3202... Generator Loss: 0.8195
Epoch 1/1... Discriminator Loss: 1.1667... Generator Loss: 0.9785
Epoch 1/1... Discriminator Loss: 1.3043... Generator Loss: 1.0896
Epoch 1/1... Discriminator Loss: 1.3318... Generator Loss: 0.7178
Epoch 1/1... Discriminator Loss: 1.0092... Generator Loss: 1.1629
Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 0.7557
Epoch 1/1... Discriminator Loss: 1.1908... Generator Loss: 1.0548
Epoch 1/1... Discriminator Loss: 1.5116... Generator Loss: 0.5515
Epoch 1/1... Discriminator Loss: 1.3612... Generator Loss: 0.6629
Epoch 1/1... Discriminator Loss: 1.1676... Generator Loss: 0.7756

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.